library(magrittr)
library(tidyverse)
library(Seurat)
library(readxl)
library(cowplot)
library(colorblindr)
library(viridis)
library(progeny)
library(destiny)
theme_cowplot2 <- function(...) {
theme_cowplot(font_size = 12, ...) %+replace%
theme(strip.background = element_blank(),
plot.background = element_blank())
}
theme_set(theme_cowplot2())
coi <- params$cell_type_super
cell_sort <- params$cell_sort
cell_type_major <- params$cell_type_major
louvain_resolution <- params$louvain_resolution
louvain_cluster <- params$louvain_cluster
### load all data ---------------------------------
source("_src/global_vars.R")
# seu_obj <- read_rds(paste0("/work/shah/isabl_data_lake/analyses/16/52/1652/celltypes/", coi, "_processed.rds"))
seu_obj <- read_rds(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/outs_pre/", coi, "_seurat_", louvain_resolution, ".rds"))
myfeatures <- c("umapharmony_1", "umapharmony_2", "sample", louvain_cluster, "doublet", "nCount_RNA", "nFeature_RNA", "percent.mt", "doublet_score")
plot_data_wrapper <- function(cluster_res) {
cluster_res <- enquo(cluster_res)
as_tibble(FetchData(seu_obj, myfeatures)) %>%
left_join(meta_tbl, by = "sample") %>%
rename(cluster = !!cluster_res) %>%
mutate(cluster = as.character(cluster),
tumor_supersite = ordered(tumor_supersite, levels = names(clrs$tumor_supersite)))
}
plot_data <- plot_data_wrapper(louvain_cluster)
helper_f <- function(x) ifelse(is.na(x), "", x)
markers_v6_super[[coi]] %>%
group_by(subtype) %>%
mutate(rank = row_number(gene)) %>%
spread(subtype, gene) %>%
mutate_all(.funs = helper_f) %>%
formattable::formattable()
| rank | CD4.T.dysfunctional | CD4.T.naive | CD4.T.reg | CD8.T | Cycling.T.NK | MT.high.T.NK | NK.CD56 | NK.Cytotoxic |
|---|---|---|---|---|---|---|---|---|
| 1 | CD4 | CCR7 | CD4 | CCL4 | CDC20 | IGKC | CD63 | ADGRG1 |
| 2 | CD40LG | CD4 | FOXP3 | CD8A | CDK1 | MALAT1 | CD7 | CX3CR1 |
| 3 | CTLA4 | IL7R | IL2RA | CD8B | MKI67 | MIAT | FCER1G | FCER1G |
| 4 | CXCL13 | KLF2 | TNFRSF4 | CRTAM | PTTG1 | MT-ND6 | GNLY | FCGR3A |
| 5 | FKBP5 | TCF7 | TRAC | GZMA | TOP2A | MTRNR2L12 | KLRC1 | FGFBP2 |
| 6 | IL6ST | GZMB | XIST | KLRD1 | GNLY | |||
| 7 | ITM2A | GZMK | KLRF1 | GZMH | ||||
| 8 | MAF | GZMM | KRT81 | IGFBP7 | ||||
| 9 | NMB | IFNG | KRT86 | KLRD1 | ||||
| 10 | NR3C1 | LAG3 | NCAM1 | KLRF1 | ||||
| 11 | PDCD1 | MT1E | NKG7 | NKG7 | ||||
| 12 | TNFRSF4 | MT1X | TYROBP | PLAC8 | ||||
| 13 | TOX2 | MT2A | XCL1 | PLEK | ||||
| 14 | TSHZ2 | PTMS | XCL2 | PTGDS | ||||
| 15 | TRGC2 | SPON2 | ||||||
| 16 | TYROBP |
# marker_tbl <- read_tsv(paste0("/work/shah/isabl_data_lake/analyses/16/52/1652/celltypes/", coi, "_markers.tsv")) %>%
# filter(resolution == louvain_resolution)
marker_tbl <- read_tsv(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/outs_pre/", coi, "_markers_", louvain_resolution, ".tsv"))
## Hypergeometric test --------------------------------------
test_set <- marker_tbl %>%
group_by(cluster) %>%
filter(!str_detect(gene, "^RPS|^RPL")) %>%
slice(1:50) %>%
mutate(k = length(cluster)) %>%
ungroup %>%
select(cluster, gene, k) %>%
mutate(join_helper = 1) %>%
group_by(cluster, join_helper, k) %>%
nest(test_set = gene)
markers_doub_tbl <- markers_v6 %>%
enframe("subtype", "gene") %>%
filter(!(subtype %in% unique(c(coi, cell_type_major)))) %>%
unnest(gene) %>%
group_by(gene) %>%
filter(length(gene) == 1) %>%
mutate(subtype = paste0("doublet.", subtype)) %>%
bind_rows(tibble(subtype = "Mito.high", gene = grep("^MT-", rownames(seu_obj), value = T)))
ref_set <- markers_v6_super[[coi]] %>%
bind_rows(markers_doub_tbl) %>%
group_by(subtype) %>%
mutate(m = length(gene),
n = length(rownames(seu_obj))-m,
join_helper = 1) %>%
group_by(subtype, m, n, join_helper) %>%
nest(ref_set = gene)
hyper_tbl <- test_set %>%
left_join(ref_set, by = "join_helper") %>%
group_by(cluster, subtype, m, n, k) %>%
do(q = length(intersect(unlist(.$ref_set), unlist(.$test_set)))) %>%
mutate(pval = 1-phyper(q = q, m = m, n = n, k = k)) %>%
ungroup %>%
mutate(qval = p.adjust(pval, "BH"),
sig = qval < 0.01)
# hyper_tbl %>%
# group_by(subtype) %>%
# filter(any(qval < 0.01)) %>%
# ggplot(aes(subtype, -log10(qval), fill = sig)) +
# geom_bar(stat = "identity") +
# facet_wrap(~cluster) +
# coord_flip()
low_rank <- str_detect(unique(hyper_tbl$subtype), "doublet")
subtype_lvl <- c(sort(unique(hyper_tbl$subtype)[!low_rank]), sort(unique(hyper_tbl$subtype)[low_rank]))
cluster_label_tbl <- hyper_tbl %>%
mutate(subtype = ordered(subtype, levels = subtype_lvl)) %>%
arrange(qval, subtype) %>%
group_by(cluster) %>%
slice(1) %>%
mutate(subtype = ifelse(sig, as.character(subtype), paste0("unknown_", cluster))) %>%
select(cluster, cluster_label = subtype) %>%
ungroup %>%
mutate(cluster_label = make.unique(cluster_label, sep = "_"))
seu_obj$cluster_label <- unname(deframe(cluster_label_tbl)[as.character(unlist(seu_obj[[paste0("RNA_snn_res.", louvain_resolution)]]))])
plot_data$cluster_label <- seu_obj$cluster_label
marker_sheet <- marker_tbl %>%
left_join(cluster_label_tbl, by = "cluster") %>%
group_by(cluster_label) %>%
filter(!str_detect(gene, "^RPS|^RPL")) %>%
slice(1:50) %>%
mutate(rank = row_number(-avg_logFC)) %>%
select(cluster_label, gene, rank) %>%
spread(cluster_label, gene) %>%
mutate_all(.funs = helper_f)
formattable::formattable(marker_sheet)
| rank | CD4.T.dysfunctional | CD4.T.naive | CD4.T.naive_1 | CD4.T.naive_2 | CD4.T.reg | CD8.T | CD8.T_1 | CD8.T_2 | CD8.T_3 | Cycling.T.NK | doublet.Fibroblast | doublet.Monocyte | doublet.Plasma.cell | NK.CD56 | NK.CD56_1 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | CXCL13 | IL7R | KLRB1 | KLF2 | TNFRSF4 | CD8A | IFIT3 | HSPA1A | XCL2 | STMN1 | DCN | HLA-DRA | SOX4 | GNLY | FGFBP2 |
| 2 | NMB | CCR7 | IL4I1 | CCR7 | IL2RA | GZMK | ISG15 | HSPA1B | GZMB | MKI67 | IGFBP5 | CST3 | PTCRA | TYROBP | FCGR3A |
| 3 | NR3C1 | KLF2 | IL7R | JUNB | FOXP3 | CD8B | MX1 | MT1X | CRTAM | TUBA1B | RBP1 | CXCL8 | MAL | AREG | SPON2 |
| 4 | FKBP5 | EEF1B2 | LTB | SELL | CTLA4 | CCL4L2 | IFIT1 | DNAJB1 | XCL1 | CENPF | CALD1 | SPP1 | MZB1 | KLRC1 | PRF1 |
| 5 | MAF | TPT1 | LST1 | DUSP1 | LTB | CCL4 | IFIT2 | MT1E | FABP5 | TOP2A | C7 | S100A9 | DNTT | FCER1G | KLRF1 |
| 6 | IL6ST | EEF1A1 | TNFSF13B | FOS | RTKN2 | CCL5 | RSAD2 | HSPA6 | TNFRSF9 | HIST1H4C | MEG3 | S100A8 | TFDP2 | TRDC | GNLY |
| 7 | ITM2A | MAL | CCR6 | IL7R | BATF | GZMH | IFI6 | HSP90AA1 | CXCL13 | TUBB | IGFBP4 | HLA-DQA1 | CD1E | XCL1 | KLRD1 |
| 8 | TSHZ2 | TCF7 | CTSH | AREG | TNFRSF18 | TRGC2 | MX2 | MT2A | HSP90AB1 | HMGB2 | ADIRF | CD74 | STMN1 | KLRD1 | NKG7 |
| 9 | CTLA4 | CD40LG | AQP3 | EEF1A1 | SAT1 | ITM2C | IFI44L | HSPH1 | PKM | ASPM | RARRES2 | LYZ | ARPP21 | KRT81 | CX3CR1 |
| 10 | CD40LG | SELL | CCL20 | CD69 | TBC1D4 | CRTAM | ISG20 | HSPE1 | GAPDH | TYMS | NR2F2 | FTL | AC084033.3 | XCL2 | GZMB |
| 11 | PDCD1 | GPR183 | NFKBIA | BTG2 | TIGIT | GZMA | HERC5 | HSPD1 | NME1 | NUSAP1 | SELENOP | APOE | CDK6 | IGFBP2 | PLAC8 |
| 12 | CD4 | LDHB | RORA | GPR183 | GADD45A | IFNG | SAMD9L | HSPB1 | IFNG | HMGN2 | MDK | HLA-DQB1 | MAP1A | CLIC3 | CLIC3 |
| 13 | LIMS1 | NOSIP | TNFRSF25 | PIK3IP1 | TNFRSF1B | HLA-DPB1 | OAS1 | HSP90AB1 | RGCC | PCLAF | TAGLN | MARCKS | AC011893.1 | IL2RB | PLEK |
| 14 | TNFRSF4 | SNHG8 | CEBPD | CD55 | PMAIP1 | CXCR6 | TNFSF10 | DNAJA1 | MIR155HG | H2AFZ | MGP | AIF1 | GLUL | CEBPD | TYROBP |
| 15 | RNF19A | PABPC1 | NCR3 | DNAJB1 | UGP2 | KLRG1 | STAT1 | JUN | TNFRSF18 | SMC4 | SOX4 | C1QB | ADA | TXK | PTGDS |
| 16 | CORO1B | NOP53 | TNFAIP3 | FKBP5 | IKZF2 | LAG3 | EIF2AK2 | CACYBP | SRM | PCNA | EGR1 | FTH1 | AL357060.1 | KRT86 | EFHD2 |
| 17 | RBPJ | LEF1 | SLC4A10 | TSC22D3 | TNFRSF9 | DTHD1 | OAS3 | HSPA8 | HSPA5 | HIST1H1B | IFITM3 | BASP1 | GRASP | CTSW | FCER1G |
| 18 | CPM | LTB | TMIGD2 | RACK1 | ICOS | CD3G | SAMD9 | FKBP4 | ENO1 | TPX2 | C11orf96 | C1QA | CD1B | KLRB1 | CST7 |
| 19 | ZBED2 | EIF3E | DPP4 | LDHB | LINC01943 | HLA-DRB1 | GBP1 | CHORDC1 | REL | DUT | CLU | C15orf48 | MIR181A1HG | MATK | ADGRG1 |
| 20 | AC004585.1 | RACK1 | TPT1 | PLAC8 | IL32 | CST7 | XAF1 | RGS2 | TPI1 | UBE2C | SFRP4 | FN1 | CCDC26 | CCL3 | GZMH |
| 21 | AHI1 | NACA | S100A4 | CXCR4 | SOX4 | CD3D | EPSTI1 | DNAJB4 | NPW | CLSPN | SPARCL1 | MNDA | JCHAIN | CD7 | CCL3 |
| 22 | DUSP4 | UBA52 | MYBL1 | SARAF | ARID5B | JAML | IFI44 | DNAJA4 | EIF5A | SMC2 | TCEAL4 | G0S2 | VIPR2 | NKG7 | HOPX |
| 23 | TOX2 | SOCS3 | CD40LG | EEF1B2 | CD27 | PTMS | PLSCR1 | ZFAND2A | RBPJ | ATAD2 | ADAMTS1 | APOC1 | ID1 | CD63 | IGFBP7 |
| 24 | ICA1 | TOMM7 | LINC01871 | TCF7 | BIRC3 | HLA-DPA1 | USP18 | ANXA1 | PARK7 | CKS1B | TIMP2 | SOD2 | SOCS2 | HOPX | ZEB2 |
| 25 | ARID5B | JUNB | SPOCK2 | TPT1 | LAYN | THEMIS | IFI35 | PPP1R15A | RGS16 | UBE2S | STAR | NPC2 | RCAN1 | TMIGD2 | PRSS23 |
| 26 | CCDC50 | SERINC5 | ERN1 | FOSB | CORO1B | DUSP2 | OASL | FOS | CD160 | TMPO | C1R | LST1 | CASC15 | CMC1 | AKR1C3 |
| 27 | CD84 | TMEM123 | JAML | SC5D | TYMP | GZMM | MT2A | FOSB | PIM3 | PTTG1 | LUM | GSN | CLDN5 | TNFRSF18 | CD247 |
| 28 | IGFL2 | EEF2 | FKBP11 | NACA | CD4 | APOBEC3G | HELZ2 | DUSP1 | CD82 | TUBB4B | CST3 | MS4A6A | PFKFB2 | KLRC2 | MYBL1 |
| 29 | BATF | FXYD5 | IFNGR1 | BTG1 | DUSP4 | LINC02446 | TRIM22 | SERPINH1 | KDM6B | KNL1 | SERPINF1 | IL1B | GALNT2 | SRGAP3 | AREG |
| 30 | RGS1 | TSHZ2 | S100A6 | CCND3 | ENTPD1 | TNIP3 | CMPK2 | TSPYL2 | RAN | DEK | WFDC2 | PSAP | HES4 | GSTP1 | S1PR5 |
| 31 | SRGN | TRABD2A | MGAT4A | NOP53 | CTSC | ITGA1 | PARP14 | UBC | TSPAN17 | CENPE | C1S | GRN | MARCKSL1 | LAT2 | CTSW |
| 32 | CH25H | SARAF | EEF1A1 | ERAP2 | MIR4435-2HG | CD27 | LY6E | UBB | NAMPT | HELLS | FHL2 | CD83 | TP53INP1 | GZMB | C1orf21 |
| 33 | SPOCK2 | ANK3 | ELK3 | ZBTB16 | LINC02099 | PPP1R14B | NT5C3A | AHSA1 | TNFRSF1B | NASP | PEG3 | CTSH | APBA2 | LINC00996 | ABHD17A |
| 34 | ZNRF1 | AQP3 | GPR183 | EEF1D | SPOCK2 | SLF1 | DDX58 | NEU1 | RANBP1 | HMGB1 | CEBPD | GLUL | NREP | PRF1 | KLF2 |
| 35 | CHN1 | RIPOR2 | KIT | PLK3 | MAGEH1 | CLEC2B | IRF7 | DNAJB6 | ZBED2 | RRM2 | NR2F1 | MEF2C | TSHR | KLRF1 | PTPN12 |
| 36 | TNFRSF25 | AP3M2 | LTC4S | PPP1R15A | PHACTR2 | IKZF3 | RNF213 | GADD45B | NDFIP2 | BIRC5 | TIMP1 | CYBB | MME | NCAM1 | TTC38 |
| 37 | CD200 | ZFAS1 | RORC | ZFP36L2 | CARD16 | CCL3L1 | DDX60L | KLF6 | GFOD1 | DLGAP5 | SERPING1 | CTSB | AC002454.1 | CXXC5 | KLRB1 |
| 38 | RILPL2 | LINC02273 | RUNX2 | EEF2 | S100A4 | LINC01871 | SAT1 | BTG2 | GOLIM4 | CKS2 | AKAP12 | CSF3R | CHI3L2 | MCTP2 | CCL4 |
| 39 | TNFRSF18 | TOB1 | ZBTB16 | HNRNPA1 | STAM | CD84 | DDX60 | BAG3 | MRTO4 | FABP5 | TSC22D1 | CD14 | SMIM3 | SH2D1B | PTGDR |
| 40 | METTL8 | EIF4B | FAM241A | VSIR | GLRX | EOMES | PPM1K | TNF | PAICS | H2AFV | TPM2 | SGK1 | SSBP2 | IFITM3 | ITGB2 |
| 41 | SLA | SESN3 | IL23R | KLF3 | SPATS2L | LYST | PNPT1 | ERN1 | DCTPP1 | H2AFX | BEX3 | C1QC | UHRF1 | ZNF683 | XBP1 |
| 42 | SMCO4 | TNFRSF25 | PDE4D | PASK | AC005224.3 | TNFSF9 | PARP9 | JUNB | NOLC1 | CDK1 | NUPR1 | SPI1 | LRRC28 | ITGA1 | CEP78 |
| 43 | BTLA | NSA2 | B3GALT2 | NPM1 | MAF | COTL1 | IFIH1 | JUND | EBNA1BP2 | GAPDH | DLK1 | FCGRT | BCL11A | IFITM2 | ARL4C |
| 44 | NAP1L4 | PASK | EEF1B2 | EIF3H | PBXIP1 | CD52 | SP110 | DEDD2 | ZNF593 | CXCL13 | IGFBP7 | EGR1 | SCAI | CCL5 | BIN2 |
| 45 | MIR155HG | FAU | TLE1 | TXNIP | F5 | ZNF683 | OAS2 | CD69 | CAMK1 | MCM7 | COL1A2 | FCGR2A | ATP6AP1L | ITGAX | LITAF |
| 46 | FYB1 | EEF1D | CERK | LDLRAP1 | SLAMF1 | HCST | C19orf66 | CLK1 | GEM | RANBP1 | CDKN1C | PLAUR | RUFY3 | CD38 | TRDC |
| 47 | PTPN13 | LDLRAP1 | CFH | CMTM8 | BTG3 | KIAA1551 | STAT2 | IER5L | EGR2 | CDKN3 | FILIP1L | CPVL | CD79A | SAMD3 | TXK |
| 48 | BIRC3 | CTSL | PERP | SCML1 | TRAC | GPR174 | LAG3 | H3F3B | CD72 | EZH2 | RARRES1 | MS4A7 | GNA15 | SLC16A3 | MYOM2 |
| 49 | SESN3 | ITGA6 | PLAT | LINC00402 | IL1R1 | CD3E | LAP3 | NR4A1 | NOP16 | GTSE1 | SLC40A1 | ALDH2 | HHIP-AS1 | CAPN12 | GZMM |
| 50 | AGFG1 | PFDN5 | KIF5C | RIPK2 | DNPH1 | PDCD1 | APOL6 | CXCR4 | METTL1 | MCM3 | TCEAL9 | SERPINA1 | MYB | CD247 | CD300A |
write_tsv(marker_sheet, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_marker_sheet.tsv"))
enframe(sort(table(seu_obj$cluster_label))) %>%
mutate(name = ordered(name, levels = rev(name))) %>%
ggplot() +
geom_bar(aes(name, value), stat = "identity") +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
labs(y = c("#cells"), x = "cluster")
alpha_lvl <- ifelse(nrow(plot_data) < 20000, 0.2, 0.1)
pt_size <- ifelse(nrow(plot_data) < 20000, 0.2, 0.05)
common_layers_disc <- list(
geom_point(size = pt_size, alpha = alpha_lvl),
NoAxes(),
guides(color = guide_legend(override.aes = list(size = 2, alpha = 1))),
labs(color = "")
)
common_layers_cont <- list(
geom_point(size = pt_size, alpha = alpha_lvl),
NoAxes(),
scale_color_gradientn(colors = viridis(9)),
guides(color = guide_colorbar())
)
ggplot(plot_data, aes(umapharmony_1, umapharmony_2, color = cluster_label)) +
common_layers_disc +
#facet_wrap(~therapy) +
ggtitle("Sub cluster")
my_subtypes <- names(clrs$cluster_label[[coi]])
my_subtypes <- c(my_subtypes, unlist(lapply(paste0("_", 1:3), function(x) paste0(my_subtypes, x)))) %>% .[!str_detect(., "doublet")]
cells_to_keep <- colnames(seu_obj)[seu_obj$cluster_label %in% my_subtypes]
seu_obj_sub <- subset(seu_obj, cells = cells_to_keep)
seu_obj_sub <- RunUMAP(seu_obj_sub, dims = 1:50, reduction = "harmony", reduction.name = "umapharmony", reduction.key = "umapharmony_")
seu_obj_sub$cluster_label <- seu_obj$cluster_label[colnames(seu_obj) %in% colnames(seu_obj_sub)]
write_rds(seu_obj_sub, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_processed_filtered.rds"))
seu_obj_sub <- read_rds(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_processed_filtered.rds"))
plot_data_sub <- as_tibble(FetchData(seu_obj_sub, c(myfeatures, "cluster_label"))) %>%
left_join(meta_tbl, by = "sample") %>%
mutate(patient_id = str_remove_all(patient_id, "SPECTRUM-OV-"),
tumor_supersite = ordered(tumor_supersite, levels = names(clrs$tumor_supersite))) %>%
mutate(cell_id = colnames(seu_obj_sub),
cluster_label = ordered(cluster_label, levels = my_subtypes),
)
if (cell_sort == "CD45+") {
plot_data_sub <- filter(plot_data_sub, sort_parameters != "singlet, live, CD45-", !is.na(tumor_supersite))
}
if (cell_sort == "CD45-") {
plot_data_sub <- filter(plot_data_sub, sort_parameters != "singlet, live, CD45+", !is.na(tumor_supersite))
}
ggplot(plot_data_sub, aes(umapharmony_1, umapharmony_2, color = cluster_label)) +
common_layers_disc +
ggtitle("Sub cluster") +
#facet_wrap(~cluster_label) +
scale_color_manual(values = clrs$cluster_label[[coi]])
ggplot(plot_data_sub, aes(umapharmony_1, umapharmony_2, color = patient_id_short)) +
common_layers_disc +
ggtitle("Patient") +
# facet_wrap(~therapy) +
scale_color_manual(values = clrs$patient_id_short)
ggplot(plot_data_sub, aes(umapharmony_1, umapharmony_2, color = tumor_supersite)) +
# geom_point(aes(umapharmony_1, umapharmony_2),
# color = "grey90", size = 0.01,
# data = select(plot_data_sub, -tumor_supersite)) +
common_layers_disc +
ggtitle("Site") +
# facet_wrap(~therapy) +
scale_color_manual(values = clrs$tumor_supersite)
write_tsv(select(plot_data_sub, cell_id, everything(), -umapharmony_1, -umapharmony_2, -contains("RNA_")), paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_embedding.tsv"))
signature_modules <- read_excel("_data/small/signatures/SPECTRUM v5 sub cluster markers.xlsx", sheet = 2, skip = 1, range = "M2:P100") %>%
gather(module, gene) %>%
na.omit() %>%
group_by(module) %>%
do(gene = c(.$gene)) %>%
{setNames(.$gene, .$module)}
signature_modules$ISG.module <- c("CCL5", "CXCL10", "IFNA1", "IFNB1", "ISG15", "IFI27L2", "SAMD9L")
## compute expression module scores
for (i in 1:length(signature_modules)) {
seu_obj_sub <- AddModuleScore(seu_obj_sub, features = signature_modules[i], name = names(signature_modules)[i])
seu_obj_sub[[names(signature_modules)[i]]] <- seu_obj_sub[[paste0(names(signature_modules)[i], "1")]]
seu_obj_sub[[paste0(names(signature_modules)[i], "1")]] <- NULL
print(paste(names(signature_modules)[i], "DONE"))
}
## [1] "CD8.Cytotoxic DONE"
## [1] "CD8.Dysfunctional DONE"
## [1] "CD8.Naive DONE"
## [1] "CD8.Predysfunctional DONE"
## [1] "ISG.module DONE"
## compute progeny scores
progeny_list <- seu_obj_sub@assays$RNA@data[VariableFeatures(seu_obj_sub),] %>%
as.matrix %>%
progeny %>%
as.data.frame %>%
as.list
names(progeny_list) <- make.names(paste0(names(progeny_list), ".pathway"))
for (i in 1:length(progeny_list)) {
seu_obj_sub <- AddMetaData(seu_obj_sub, metadata = progeny_list[[i]],
col.name = names(progeny_list)[i])
}
write_rds(seu_obj_sub, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_processed_filtered_annotated.rds"))
marker_top_tbl <- marker_sheet[,-1] %>%
slice(1:10) %>%
as.list %>%
.[!str_detect(names(.), "doublet")] %>%
enframe("cluster_label_x", "gene") %>%
unnest(gene)
plot_data_markers <- as_tibble(FetchData(seu_obj_sub, c("cluster_label", myfeatures, unique(marker_top_tbl$gene)))) %>%
gather(gene, value, -c(1:(length(myfeatures)+1))) %>%
left_join(meta_tbl, by = "sample") %>%
mutate(patient_id = str_remove_all(patient_id, "SPECTRUM-OV-"),
tumor_supersite = ordered(tumor_supersite, levels = names(clrs$tumor_supersite))) %>%
mutate(cluster_label = ordered(cluster_label, levels = my_subtypes)) %>%
group_by(cluster_label, gene) %>%
summarise(value = mean(value, na.rm = T)) %>%
group_by(gene) %>%
mutate(value = scales::rescale(value)) %>%
left_join(marker_top_tbl, by = "gene") %>%
mutate(cluster_label_x = ordered(cluster_label_x, levels = rev(names(clrs$cluster_label[[coi]]))))
ggplot(plot_data_markers) +
geom_tile(aes(gene, cluster_label, fill = value)) +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
facet_grid(~cluster_label_x, scales = "free", space = "free") +
scale_fill_gradientn(colors = viridis(9)) +
labs(fill = "Scaled\nexpression") +
theme(aspect.ratio = 1,
axis.line = element_blank(),
axis.ticks = element_blank(),
axis.title = element_blank())
# ggsave(paste0("_fig/002_marker_heatmap_", coi, ".pdf"), width = nrow(marker_top_tbl)/6, height = 5)
comp_site_tbl <- plot_data_sub %>%
filter(!is.na(tumor_supersite)) %>%
group_by(cluster_label, tumor_supersite) %>%
tally %>%
group_by(tumor_supersite) %>%
mutate(nrel = n/sum(n)*100) %>%
ungroup
pnrel_site <- ggplot(comp_site_tbl) +
geom_bar(aes(tumor_supersite, nrel, fill = cluster_label),
stat = "identity", position = position_stack()) +
scale_y_continuous(expand = c(0, 0)) +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
labs(fill = "Cluster", y = "Fraction [%]", x = "") +
scale_fill_manual(values = clrs$cluster_label[[coi]])
pnabs_site <- ggplot(comp_site_tbl) +
geom_bar(aes(tumor_supersite, n, fill = cluster_label),
stat = "identity", position = position_stack()) +
scale_y_continuous(expand = c(0, 0)) +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
labs(fill = "Cluster", y = "# cells", x = "") +
scale_fill_manual(values = clrs$cluster_label[[coi]])
plot_grid(pnabs_site, pnrel_site, ncol = 2, align = "h")
# ggsave(paste0("_fig/02_deep_dive_", coi, "_comp_site.pdf"), width = 8, height = 4)
comp_tbl_sample_sort <- plot_data_sub %>%
group_by(tumor_subsite, tumor_supersite, patient_id, consensus_signature, therapy, cluster_label) %>%
tally %>%
group_by(tumor_subsite, tumor_supersite, patient_id, consensus_signature, therapy) %>%
mutate(nrel = n/sum(n)*100,
nsum = sum(n),
log10n = log10(n),
label_supersite = "Site",
label_mutsig = "Signature",
label_therapy = "Rx") %>%
ungroup %>%
arrange(desc(therapy), tumor_supersite) %>%
mutate(tumor_subsite_rx = paste0(tumor_subsite, "_", therapy)) %>%
mutate(tumor_subsite = ordered(tumor_subsite, levels = unique(tumor_subsite)),
tumor_subsite_rx = ordered(tumor_subsite_rx, levels = unique(tumor_subsite_rx))) %>%
arrange(patient_id) %>%
mutate(label_patient_id = ifelse(as.logical(as.numeric(fct_inorder(as.character(patient_id)))%%2), "Patient1", "Patient2"))
sample_id_x_tbl <- plot_data_sub %>%
mutate(sort_short_x = cell_sort) %>%
distinct(patient_id, sort_short_x, tumor_subsite, therapy, sample) %>%
unite(sample_id_x, patient_id, sort_short_x, tumor_subsite, therapy) %>%
arrange(sample_id_x)
comp_tbl_sample_sort %>%
mutate(sort_short_x = cell_sort) %>%
unite(sample_id_x, patient_id, sort_short_x, tumor_subsite_rx) %>%
select(sample_id_x, cluster_label, n, nrel, nsum) %>%
left_join(sample_id_x_tbl, by = "sample_id_x") %>%
write_tsv(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_subtype_compositions.tsv"))
ybreaks <- c(500, 1000, 2000, 4000, 6000, 8000, 10000, 15000, 20000)
max_cells_per_sample <- max(comp_tbl_sample_sort$nsum)
ymaxn <- ybreaks[max_cells_per_sample < ybreaks][1]
comp_plot_wrapper <- function(y = "nrel", switch = NULL) {
if (y == "nrel") ylab <- paste0("Fraction\nof cells [%]")
if (y == "n") ylab <- paste0("Number\nof cells")
p <- ggplot(comp_tbl_sample_sort,
aes_string("tumor_subsite_rx", y, fill = "cluster_label")) +
facet_grid(~patient_id, space = "free", scales = "free", switch = switch) +
coord_cartesian(clip = "off") +
scale_fill_manual(values = clrs$cluster_label[[coi]]) +
theme(axis.text.x = element_blank(),
axis.title.y = element_text(angle = 0, vjust = 0.5, hjust = 0.5,
margin = margin(0, -0.4, 0, 0, unit = "npc")),
axis.ticks.x = element_blank(),
axis.title.x = element_blank(),
axis.line.x = element_blank(),
strip.text.y = element_blank(),
strip.text.x = element_blank(),
strip.background.y = element_blank(),
strip.background.x = element_blank(),
plot.margin = margin(0, 0, 0, 0, "npc")) +
labs(x = "",
y = ylab) +
guides(fill = FALSE)
if (y == "nrel") p <- p +
geom_bar(stat = "identity") +
scale_y_continuous(expand = c(0, 0),
breaks = c(0, 50, 100),
labels = c("0", "50", "100"))
if (y == "n") p <- p +
geom_bar(stat = "identity", position = position_stack()) +
scale_y_continuous(expand = c(0, 0),
breaks = c(0, ymaxn/2, ymaxn),
limits = c(0, ymaxn),
labels = c("", ymaxn/2, ymaxn)) +
expand_limits(y = c(0, ymaxn)) +
theme(panel.grid.major.y = element_line(linetype = 1, color = "grey90", size = 0.5))
return(p)
}
common_label_layers <- list(
geom_tile(color = "white", size = 0.15),
theme(axis.text.x = element_blank(),
axis.ticks = element_blank(),
axis.title.x = element_blank(),
axis.line.x = element_blank(),
strip.text = element_blank(),
strip.background = element_blank(),
plot.margin = margin(0, 0, 0, 0, "npc")),
scale_y_discrete(expand = c(0, 0)),
labs(y = ""),
guides(fill = FALSE),
facet_grid(~patient_id,
space = "free", scales = "free")
)
comp_label_site <- ggplot(distinct(comp_tbl_sample_sort, tumor_subsite_rx, therapy, tumor_supersite, label_supersite, patient_id),
aes(tumor_subsite_rx, label_supersite,
fill = tumor_supersite)) +
scale_fill_manual(values = clrs$tumor_supersite) +
common_label_layers
comp_label_rx <- ggplot(distinct(comp_tbl_sample_sort, tumor_subsite_rx, therapy, tumor_supersite, label_therapy, consensus_signature, patient_id),
aes(tumor_subsite_rx, label_therapy,
fill = therapy)) +
scale_fill_manual(values = c(`post-Rx` = "gold3", `pre-Rx` = "steelblue")) +
common_label_layers
comp_label_mutsig <- ggplot(distinct(comp_tbl_sample_sort, tumor_subsite_rx, therapy, tumor_supersite, label_mutsig, consensus_signature, patient_id),
aes(tumor_subsite_rx, label_mutsig,
fill = consensus_signature)) +
scale_fill_manual(values = clrs$consensus_signature) +
common_label_layers
patient_label_tbl <- distinct(comp_tbl_sample_sort, patient_id, .keep_all = T)
comp_label_patient_id <- ggplot(patient_label_tbl, aes(tumor_subsite_rx, label_patient_id)) +
scale_fill_manual(values = clrs$consensus_signature) +
geom_text(aes(tumor_subsite_rx, label_patient_id, label = patient_id)) +
facet_grid(~patient_id,
space = "free", scales = "free") +
coord_cartesian(clip = "off") +
theme_void() +
theme(strip.text = element_blank(),
strip.background = element_blank(),
plot.margin = margin(0, 0, 0, 0, "npc"))
hist_plot_wrapper <- function(x = "nrel") {
if (x == "nrel") {
xlab <- paste0("Fraction of cells [%]")
bw <- 5
}
if (x == "log10n") {
xlab <- paste0("Number of cells")
bw <- 0.2
}
p <- ggplot(comp_tbl_sample_sort) +
ggridges::geom_density_ridges(
aes_string(x, "cluster_label", fill = "cluster_label"), color = "black",
stat = "binline", binwidth = bw, scale = 3) +
facet_grid(label_supersite~.,
space = "free", scales = "free") +
scale_fill_manual(values = clrs$cluster_label[[coi]]) +
theme(axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
axis.title.y = element_blank(),
axis.line.y = element_blank(),
strip.text = element_blank(),
strip.background = element_blank(),
plot.margin = margin(0, 0, 0, 0, "npc")) +
scale_x_continuous(expand = c(0, 0)) +
scale_y_discrete(expand = c(0, 0)) +
guides(fill = FALSE) +
labs(x = xlab)
if (x == "log10n") p <- p + expand_limits(x = c(0, 3)) +
scale_x_continuous(expand = c(0, 0),
labels = function(x) parse(text = paste("10^", x)))
return(p)
}
pcomp1 <- comp_plot_wrapper("n")
pcomp2 <- comp_plot_wrapper("nrel")
pcomp_grid <- plot_grid(comp_label_patient_id,
pcomp1, pcomp2,
comp_label_site, comp_label_rx, comp_label_mutsig,
ncol = 1, align = "v",
rel_heights = c(0.15, 0.33, 0.33, 0.06, 0.06, 0.06))
phist1 <- hist_plot_wrapper("log10n")
pcomp_hist_grid <- ggdraw() +
draw_plot(pcomp_grid, x = 0.01, y = 0, width = 0.85, height = 1) +
draw_plot(phist1, x = 0.87, y = 0.05, width = 0.12, height = 0.8)
pcomp_hist_grid
# ggsave(paste0("_fig/02_composition_v6_",coi,".pdf"), pcomp_hist_grid, width = 10, height = 2)
comp_tbl_z <- comp_tbl_sample_sort %>%
filter(therapy == "pre-Rx",
!(tumor_supersite %in% c("Ascites", "Other"))) %>%
group_by(patient_id, cluster_label) %>%
arrange(patient_id, cluster_label, nrel) %>%
mutate(rank = row_number(nrel),
z_rank = scales::rescale(rank)) %>%
mutate(mean_nrel = mean(nrel, na.rm = T),
sd_nrel = sd(nrel, na.rm = T),
z_nrel = (nrel - mean_nrel) / sd_nrel) %>%
ungroup()
ggplot(comp_tbl_z) +
geom_boxplot(aes(tumor_supersite, z_nrel, color = tumor_supersite),
outlier.shape = NA) +
geom_point(aes(tumor_supersite, z_nrel, color = tumor_supersite), position = position_jitter(), size = 0.1) +
facet_grid(~cluster_label, scales = "free_x", space = "free_x") +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
aspect.ratio = 5) +
scale_color_manual(values = clrs$tumor_supersite)
ggplot(comp_tbl_z) +
geom_boxplot(aes(tumor_supersite, z_rank, color = tumor_supersite),
outlier.shape = NA) +
geom_point(aes(tumor_supersite, z_rank, color = tumor_supersite), position = position_jitter(), size = 0.1) +
facet_grid(~cluster_label, scales = "free_x", space = "free_x") +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
aspect.ratio = 5) +
scale_color_manual(values = clrs$tumor_supersite)
seu_obj_cd8 <- seu_obj_sub %>%
subset(subset = cluster_label == "CD8.T") %>%
FindNeighbors(reduction = "harmony", dims = 1:50) %>%
FindClusters(resolution = 0.2) %>%
RunUMAP(dims = 1:50, reduction = "harmony", reduction.name = "umapharmony", reduction.key = "umapharmony_")
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 74288
## Number of edges: 2112884
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.8715
## Number of communities: 18
## Elapsed time: 20 seconds
write_rds(seu_obj_cd8, "/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/CD8.T_processed.rds")
seu_obj_cd8 <- read_rds("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/CD8.T_processed.rds")
marker_tbl <- FindAllMarkers(seu_obj_cd8, only.pos = T)
write_tsv(as_tibble(marker_tbl), "/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/CD8.T_markers.tsv")
marker_tbl <- read_tsv("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/CD8.T_markers.tsv")
## Hypergeometric test --------------------------------------
test_set <- marker_tbl %>%
group_by(cluster) %>%
filter(!str_detect(gene, "^RPS|^RPL")) %>%
slice(1:50) %>%
mutate(k = length(cluster)) %>%
ungroup %>%
select(cluster, gene, k) %>%
mutate(join_helper = 1) %>%
group_by(cluster, join_helper, k) %>%
nest(test_set = gene)
markers_doub_tbl <- markers_v6 %>%
enframe("subtype", "gene") %>%
filter(!(subtype %in% unique(c(coi, cell_type_major)))) %>%
unnest(gene) %>%
group_by(gene) %>%
filter(length(gene) == 1) %>%
mutate(subtype = paste0("doublet.", subtype)) %>%
bind_rows(tibble(subtype = "Mito.high", gene = grep("^MT-", rownames(seu_obj), value = T)))
ref_set <- markers_v6_super[["CD8.T"]] %>%
bind_rows(markers_doub_tbl) %>%
group_by(subtype) %>%
mutate(m = length(gene),
n = length(rownames(seu_obj))-m,
join_helper = 1) %>%
group_by(subtype, m, n, join_helper) %>%
nest(ref_set = gene)
hyper_tbl <- test_set %>%
left_join(ref_set, by = "join_helper") %>%
group_by(cluster, subtype, m, n, k) %>%
do(q = length(intersect(unlist(.$ref_set), unlist(.$test_set)))) %>%
mutate(pval = 1-phyper(q = q, m = m, n = n, k = k)) %>%
ungroup %>%
mutate(qval = p.adjust(pval, "BH"),
sig = qval < 0.01)
# hyper_tbl %>%
# group_by(subtype) %>%
# filter(any(qval < 0.01)) %>%
# ggplot(aes(subtype, -log10(qval), fill = sig)) +
# geom_bar(stat = "identity") +
# facet_wrap(~cluster) +
# coord_flip()
low_rank <- str_detect(unique(hyper_tbl$subtype), "Mito|doublet")
subtype_lvl <- c(sort(unique(hyper_tbl$subtype)[!low_rank]), sort(unique(hyper_tbl$subtype)[low_rank]))
cluster_label_tbl <- hyper_tbl %>%
mutate(subtype = ordered(subtype, levels = subtype_lvl)) %>%
arrange(qval, subtype) %>%
group_by(cluster) %>%
slice(1) %>%
mutate(subtype = ifelse(sig, as.character(subtype), paste0("unknown_", cluster))) %>%
select(cluster, cluster_label = subtype) %>%
ungroup %>%
mutate(cluster_label = make.unique(cluster_label, sep = "_"))
marker_sheet <- marker_tbl %>%
left_join(cluster_label_tbl, by = "cluster") %>%
group_by(cluster_label) %>%
filter(!str_detect(gene, "^RPS|^RPL")) %>%
slice(1:50) %>%
mutate(rank = row_number()) %>%
select(cluster_label, gene, rank) %>%
spread(cluster_label, gene) %>%
mutate_all(.funs = helper_f)
formattable::formattable(marker_sheet)
| rank | CD8.T.activated.FOS | CD8.T.activated.FOS_1 | CD8.T.dysfunctional.CXCL13 | CD8.T.naive | gd.T.cell | Mito.high |
|---|---|---|---|---|---|---|
| 1 | FOS | CCL4L2 | CXCL13 | GZMK | GNLY | MALAT1 |
| 2 | IL7R | IFNG | GZMB | CMC1 | TRDC | XIST |
| 3 | FOSB | CCL3 | CTLA4 | ITM2C | XCL1 | PTPRC |
| 4 | JUN | CCL4 | HAVCR2 | KLRG1 | HOPX | N4BP2L2 |
| 5 | KLF2 | TNF | KRT86 | GIMAP7 | ZNF683 | MT-ND3 |
| 6 | NFKBIA | CCL3L1 | CCL3 | CST7 | KLRC2 | MTRNR2L12 |
| 7 | JUNB | NFKBID | RBPJ | PLEK | CD7 | RNF213 |
| 8 | DUSP1 | EGR2 | TNFRSF9 | GIMAP4 | LGALS1 | NEAT1 |
| 9 | EGR1 | TNFSF9 | PHLDA1 | GZMM | XCL2 | DDX17 |
| 10 | TNFAIP3 | NR4A2 | MIR155HG | PPP1R14B | LINC02446 | MT-ND1 |
| 11 | TOB1 | FOS | TIGIT | SH2D1A | S100A4 | MT-CO2 |
| 12 | CD69 | FOSB | ENTPD1 | LYAR | CAPG | MT-CO3 |
| 13 | TSC22D3 | NR4A3 | TNFRSF18 | CD63 | AAK1 | |
| 14 | LTB | NR4A1 | SRGAP3 | IFITM2 | IKZF1 | |
| 15 | AC020916.1 | EGR3 | CCND2 | NCR3 | MT-CO1 | |
| 16 | ANXA1 | FASLG | CD63 | S100A6 | PNISR | |
| 17 | CCR7 | EGR1 | FAM3C | CTSW | NKTR | |
| 18 | LMNA | KDM6B | PTMS | CD9 | FUS | |
| 19 | TPT1 | RILPL2 | LAYN | IFITM3 | STK4 | |
| 20 | EEF1A1 | TAGAP | LAG3 | CD52 | CD44 | |
| 21 | MT-ND2 | CD69 | SPRY1 | KLRC1 | PLCG2 | |
| 22 | CD40LG | GADD45B | GAPDH | TIMP1 | ATM | |
| 23 | AQP3 | SLA | NDFIP2 | LTB | POLR2J3-1 | |
| 24 | CD55 | SRGN | SAMSN1 | VIM | ARGLU1 | |
| 25 | PLAC8 | ZFP36L1 | AKAP5 | KLRD1 | LUC7L3 | |
| 26 | EEF1B2 | XCL2 | CLNK | LINC01871 | TTC14 | |
| 27 | MT-ND1 | DUSP6 | GOLIM4 | GLUL | MT-ND6 | |
| 28 | PTGER2 | KLF6 | TNFRSF1B | C1orf162 | KANSL1 | |
| 29 | TIMP1 | JUND | ID2 | TRGC2 | RSRP1 | |
| 30 | ZFP36 | RASGEF1B | ACP5 | SPRY1 | IGKC | |
| 31 | GPR183 | MAP3K8 | TNFSF4 | ACTB | PCSK7 | |
| 32 | BTG2 | IER2 | ADGRG1 | LGALS3 | MT-ND4L | |
| 33 | SOCS3 | REL | MYO7A | IL32 | LINC00861 | |
| 34 | DPP4 | CRTAM | PDCD1 | ANKRD28 | SF1 | |
| 35 | TXK | PPP1R15A | LINC01871 | CTSA | MACF1 | |
| 36 | JUND | AC020916.1 | ITGAE | DSTN | MDM4 | |
| 37 | PPP1R15A | SDCBP | DUSP4 | GZMB | CCNL1 | |
| 38 | KDM6B | DDX3X | CD7 | S100A11 | MIAT | |
| 39 | MT-CYB | CD160 | VCAM1 | PRMT9 | MTRNR2L8 | |
| 40 | KLRB1 | XCL1 | SNAP47 | CKLF | ARID1B | |
| 41 | SATB1 | AC136475.3 | IFNG | TRGC1 | SORL1 | |
| 42 | MYBL1 | FAM53C | RGS1 | IKZF2 | FTX | |
| 43 | TCF7 | TNFSF14 | LINC01943 | S100A10 | TNRC6B | |
| 44 | RORA | RGCC | CSF1 | ACTG1 | PNN | |
| 45 | AREG | IRF4 | SNX9 | ID2 | CELF2 | |
| 46 | ZFP36L2 | DUSP2 | HLA-DRA | MATK | HNRNPH1 | |
| 47 | IER2 | RNF19A | DGKH | TAGLN2 | IFI6 | |
| 48 | TMEM123 | NEU1 | PRF1 | TNFRSF18 | PARP14 | |
| 49 | NR4A1 | EVI2A | BCL2L11 | XBP1 | IFI44L | |
| 50 | IER5 | TNFAIP3 | FABP5 | LDLRAD4 | TAPBP |
write_tsv(marker_sheet, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/CD8.T.cell_marker_sheet.tsv"))
seu_obj_cd8$cluster_label <- unname(deframe(cluster_label_tbl)[as.character(unlist(seu_obj_cd8[[paste0("RNA_snn_res.", louvain_resolution)]]))])
seu_obj_sub_sub <- seu_obj_sub
cluster_label_tbl_1 <- as_tibble(cbind(cell_id=colnames(seu_obj_sub), FetchData(seu_obj_sub, c("cluster_label"))))
cluster_label_tbl_2 <- as_tibble(cbind(cell_id=colnames(seu_obj_cd8), FetchData(seu_obj_cd8, c("cluster_label"))))
cluster_label_tbl <- left_join(cluster_label_tbl_1, cluster_label_tbl_2, by = "cell_id") %>%
mutate(cluster_label = ifelse(is.na(cluster_label.y), cluster_label.x, cluster_label.y))
seu_obj_sub_sub$cluster_label <- cluster_label_tbl$cluster_label
seu_obj_sub_sub <- subset(seu_obj_sub_sub, subset = cluster_label != "doublet.Plasma.cell")
write_rds(seu_obj_sub_sub, "/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/T.cell_processed_filtered_sub.rds")
root_cell <- "SPECTRUM-OV-036_S1_CD45P_PELVIC_PERITONEUM_CGGACACCAACGACTT"
seu_obj_cd8_sub <- subset(seu_obj_cd8, subset = cluster_label != "doublet.Plasma.cell" & cluster_label != "gd.T.cell")
seu_obj_cd8_sub <- subset(seu_obj_cd8_sub, cells = c(root_cell, colnames(seu_obj_cd8_sub)[colnames(seu_obj_cd8_sub)!=root_cell][-1]))
write_rds(seu_obj_sub_sub, "/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/T.cell_processed_filtered_sub.rds")
seu_obj_sub_sub <- read_rds("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/T.cell_processed_filtered_sub.rds")
dc_obj <- DiffusionMap(seu_obj_cd8_sub@reductions$harmony@cell.embeddings, k = 100)
dc_mat <- dc_obj@eigenvectors
colnames(dc_mat) <- paste0("DC_", 1:ncol(dc_mat))
seu_obj_cd8_sub[["DC"]] <- CreateDimReducObject(embeddings = dc_mat, key = "DC_", assay = DefaultAssay(seu_obj_cd8_sub))
write_rds(seu_obj_cd8_sub, "/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/CD8.T_processed_filtered.rds")
dpt_obj <- DPT(dc_obj)
write_rds(dpt_obj, "/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/CD8.T_sub_dpt.rds")
seu_obj_cd8_sub$DPT1 <- dpt_obj$DPT1
write_rds(seu_obj_cd8_sub, "/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/CD8.T_processed_filtered.rds")
seu_obj_cd8_sub <- read_rds("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/CD8.T_processed_filtered.rds")
plot_data_sub_sub <- as_tibble(FetchData(seu_obj_sub_sub, c(myfeatures, "cluster_label"))) %>%
left_join(meta_tbl, by = "sample") %>%
mutate(patient_id = str_remove_all(patient_id, "SPECTRUM-OV-"),
tumor_supersite = ordered(tumor_supersite, levels = names(clrs$tumor_supersite))) %>%
mutate(cell_id = colnames(seu_obj_sub_sub),
cluster_label = ordered(cluster_label, levels = my_subtypes),
)
if (cell_sort == "CD45+") {
plot_data_sub_sub <- filter(plot_data_sub_sub, sort_parameters != "singlet, live, CD45-", !is.na(tumor_supersite))
}
if (cell_sort == "CD45-") {
plot_data_sub_sub <- filter(plot_data_sub_sub, sort_parameters != "singlet, live, CD45+", !is.na(tumor_supersite))
}
ggplot(plot_data_sub_sub, aes(umapharmony_1, umapharmony_2, color = cluster_label)) +
common_layers_disc +
ggtitle("Sub cluster") +
#facet_wrap(~cluster_label) +
scale_color_manual(values = clrs$cluster_label[[coi]])
ggplot(plot_data_sub_sub, aes(umapharmony_1, umapharmony_2, color = patient_id_short)) +
common_layers_disc +
ggtitle("Patient") +
# facet_wrap(~therapy) +
scale_color_manual(values = clrs$patient_id_short)
ggplot(plot_data_sub_sub, aes(umapharmony_1, umapharmony_2, color = tumor_supersite)) +
# geom_point(aes(umapharmony_1, umapharmony_2),
# color = "grey90", size = 0.01,
# data = select(plot_data_sub_sub, -tumor_supersite)) +
common_layers_disc +
ggtitle("Site") +
# facet_wrap(~therapy) +
scale_color_manual(values = clrs$tumor_supersite)
write_tsv(select(plot_data_sub_sub, cell_id, everything(), -umapharmony_1, -umapharmony_2, -contains("RNA_")), paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_embedding_sub.tsv"))
plot_data_cd8_sub <- as_tibble(FetchData(seu_obj_cd8_sub, c(myfeatures, "cluster_label", "DC_1", "DC_2"))) %>%
left_join(meta_tbl, by = "sample") %>%
mutate(patient_id = str_remove_all(patient_id, "SPECTRUM-OV-"),
tumor_supersite = ordered(tumor_supersite, levels = names(clrs$tumor_supersite))) %>%
mutate(cell_id = colnames(seu_obj_cd8_sub),
cluster_label = ordered(cluster_label, levels = my_subtypes),
)
if (cell_sort == "CD45+") {
plot_data_cd8_sub <- filter(plot_data_cd8_sub, sort_parameters != "singlet, live, CD45-", !is.na(tumor_supersite))
}
if (cell_sort == "CD45-") {
plot_data_cd8_sub <- filter(plot_data_cd8_sub, sort_parameters != "singlet, live, CD45+", !is.na(tumor_supersite))
}
ggplot(plot_data_cd8_sub, aes(umapharmony_1, umapharmony_2, color = cluster_label)) +
common_layers_disc +
ggtitle("Sub cluster") +
#facet_wrap(~cluster_label) +
scale_color_manual(values = clrs$cluster_label[[coi]])
ggplot(plot_data_cd8_sub, aes(umapharmony_1, umapharmony_2, color = patient_id_short)) +
common_layers_disc +
ggtitle("Patient") +
# facet_wrap(~therapy) +
scale_color_manual(values = clrs$patient_id_short)
ggplot(plot_data_cd8_sub, aes(umapharmony_1, umapharmony_2, color = tumor_supersite)) +
common_layers_disc +
ggtitle("Site") +
# facet_wrap(~therapy) +
scale_color_manual(values = clrs$tumor_supersite)
ggplot(plot_data_cd8_sub, aes(DC_1, DC_2, color = cluster_label)) +
common_layers_disc +
ggtitle("Sub cluster") +
#facet_wrap(~cluster_label) +
scale_color_manual(values = clrs$cluster_label[[coi]])
ggplot(plot_data_cd8_sub, aes(DC_1, DC_2, color = patient_id_short)) +
common_layers_disc +
ggtitle("Patient") +
# facet_wrap(~therapy) +
scale_color_manual(values = clrs$patient_id_short)
ggplot(plot_data_cd8_sub, aes(DC_1, DC_2, color = tumor_supersite)) +
common_layers_disc +
ggtitle("Site") +
# facet_wrap(~therapy) +
scale_color_manual(values = clrs$tumor_supersite)
devtools::session_info()
## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 3.6.2 (2019-12-12)
## os Debian GNU/Linux 10 (buster)
## system x86_64, linux-gnu
## ui X11
## language (EN)
## collate en_US.UTF-8
## ctype en_US.UTF-8
## tz Etc/UTC
## date 2021-01-11
##
## ─ Packages ───────────────────────────────────────────────────────────────────
## package * version date lib
## abind 1.4-5 2016-07-21 [2]
## ape 5.3 2019-03-17 [2]
## assertthat 0.2.1 2019-03-21 [2]
## backports 1.1.10 2020-09-15 [1]
## bibtex 0.4.2.2 2020-01-02 [2]
## Biobase 2.46.0 2019-10-29 [2]
## BiocGenerics 0.32.0 2019-10-29 [2]
## BiocParallel 1.20.1 2019-12-21 [2]
## bitops 1.0-6 2013-08-17 [2]
## boot 1.3-24 2019-12-20 [3]
## broom 0.7.2 2020-10-20 [1]
## callr 3.4.2 2020-02-12 [1]
## car 3.0-8 2020-05-21 [1]
## carData 3.0-4 2020-05-22 [1]
## caTools 1.17.1.4 2020-01-13 [2]
## cellranger 1.1.0 2016-07-27 [2]
## class 7.3-15 2019-01-01 [3]
## cli 2.0.2 2020-02-28 [1]
## cluster 2.1.0 2019-06-19 [3]
## codetools 0.2-16 2018-12-24 [3]
## colorblindr * 0.1.0 2020-01-13 [2]
## colorspace * 1.4-2 2019-12-29 [2]
## cowplot * 1.0.0 2019-07-11 [2]
## crayon 1.3.4 2017-09-16 [1]
## curl 4.3 2019-12-02 [2]
## data.table 1.12.8 2019-12-09 [2]
## DBI 1.1.0 2019-12-15 [2]
## dbplyr 2.0.0 2020-11-03 [1]
## DelayedArray 0.12.2 2020-01-06 [2]
## DEoptimR 1.0-8 2016-11-19 [1]
## desc 1.2.0 2018-05-01 [2]
## destiny * 3.0.1 2020-01-16 [1]
## devtools 2.2.1 2019-09-24 [2]
## digest 0.6.25 2020-02-23 [1]
## dplyr * 1.0.2 2020-08-18 [1]
## e1071 1.7-3 2019-11-26 [1]
## ellipsis 0.3.1 2020-05-15 [1]
## evaluate 0.14 2019-05-28 [2]
## fansi 0.4.1 2020-01-08 [2]
## farver 2.0.3 2020-01-16 [1]
## fitdistrplus 1.0-14 2019-01-23 [2]
## forcats * 0.5.0 2020-03-01 [1]
## foreign 0.8-74 2019-12-26 [3]
## formattable 0.2.0.1 2016-08-05 [1]
## fs 1.5.0 2020-07-31 [1]
## future 1.15.1 2019-11-25 [2]
## future.apply 1.4.0 2020-01-07 [2]
## gbRd 0.4-11 2012-10-01 [2]
## gdata 2.18.0 2017-06-06 [2]
## generics 0.0.2 2018-11-29 [2]
## GenomeInfoDb 1.22.0 2019-10-29 [2]
## GenomeInfoDbData 1.2.2 2020-01-14 [2]
## GenomicRanges 1.38.0 2019-10-29 [2]
## ggplot.multistats 1.0.0 2019-10-28 [1]
## ggplot2 * 3.3.2 2020-06-19 [1]
## ggrepel 0.8.1 2019-05-07 [2]
## ggridges 0.5.2 2020-01-12 [2]
## ggthemes 4.2.0 2019-05-13 [1]
## globals 0.12.5 2019-12-07 [2]
## glue 1.3.2 2020-03-12 [1]
## gplots 3.0.1.2 2020-01-11 [2]
## gridExtra 2.3 2017-09-09 [2]
## gtable 0.3.0 2019-03-25 [2]
## gtools 3.8.1 2018-06-26 [2]
## haven 2.3.1 2020-06-01 [1]
## hexbin 1.28.0 2019-11-11 [2]
## hms 0.5.3 2020-01-08 [1]
## htmltools 0.4.0 2019-10-04 [2]
## htmlwidgets 1.5.1 2019-10-08 [2]
## httr 1.4.2 2020-07-20 [1]
## ica 1.0-2 2018-05-24 [2]
## igraph 1.2.5 2020-03-19 [1]
## IRanges 2.20.2 2020-01-13 [2]
## irlba 2.3.3 2019-02-05 [2]
## jsonlite 1.7.1 2020-09-07 [1]
## KernSmooth 2.23-16 2019-10-15 [3]
## knitr 1.26 2019-11-12 [2]
## knn.covertree 1.0 2019-10-28 [1]
## labeling 0.3 2014-08-23 [2]
## laeken 0.5.1 2020-02-05 [1]
## lattice 0.20-38 2018-11-04 [3]
## lazyeval 0.2.2 2019-03-15 [2]
## leiden 0.3.1 2019-07-23 [2]
## lifecycle 0.2.0 2020-03-06 [1]
## listenv 0.8.0 2019-12-05 [2]
## lmtest 0.9-37 2019-04-30 [2]
## lsei 1.2-0 2017-10-23 [2]
## lubridate 1.7.9.2 2020-11-13 [1]
## magrittr * 2.0.1 2020-11-17 [1]
## MASS 7.3-51.5 2019-12-20 [3]
## Matrix 1.2-18 2019-11-27 [3]
## matrixStats 0.56.0 2020-03-13 [1]
## memoise 1.1.0 2017-04-21 [2]
## metap 1.2 2019-12-08 [2]
## mnormt 1.5-5 2016-10-15 [2]
## modelr 0.1.8 2020-05-19 [1]
## multcomp 1.4-12 2020-01-10 [2]
## multtest 2.42.0 2019-10-29 [2]
## munsell 0.5.0 2018-06-12 [2]
## mutoss 0.1-12 2017-12-04 [2]
## mvtnorm 1.0-12 2020-01-09 [2]
## nlme 3.1-143 2019-12-10 [3]
## nnet 7.3-12 2016-02-02 [3]
## npsurv 0.4-0 2017-10-14 [2]
## numDeriv 2016.8-1.1 2019-06-06 [2]
## openxlsx 4.1.5 2020-05-06 [1]
## pbapply 1.4-2 2019-08-31 [2]
## pcaMethods 1.78.0 2019-10-29 [2]
## pillar 1.4.6 2020-07-10 [1]
## pkgbuild 1.0.6 2019-10-09 [2]
## pkgconfig 2.0.3 2019-09-22 [1]
## pkgload 1.0.2 2018-10-29 [2]
## plotly 4.9.1 2019-11-07 [2]
## plotrix 3.7-7 2019-12-05 [2]
## plyr 1.8.5 2019-12-10 [2]
## png 0.1-7 2013-12-03 [2]
## prettyunits 1.1.1 2020-01-24 [1]
## processx 3.4.2 2020-02-09 [1]
## progeny * 1.11.3 2020-10-22 [1]
## proxy 0.4-24 2020-04-25 [1]
## ps 1.3.2 2020-02-13 [1]
## purrr * 0.3.4 2020-04-17 [1]
## R.methodsS3 1.7.1 2016-02-16 [2]
## R.oo 1.23.0 2019-11-03 [2]
## R.utils 2.9.2 2019-12-08 [2]
## R6 2.4.1 2019-11-12 [1]
## ranger 0.12.1 2020-01-10 [1]
## RANN 2.6.1 2019-01-08 [2]
## rappdirs 0.3.1 2016-03-28 [2]
## RColorBrewer 1.1-2 2014-12-07 [2]
## Rcpp 1.0.4 2020-03-17 [1]
## RcppAnnoy 0.0.16 2020-03-08 [1]
## RcppEigen 0.3.3.7.0 2019-11-16 [2]
## RcppHNSW 0.2.0 2019-09-20 [2]
## RcppParallel 4.4.4 2019-09-27 [2]
## RCurl 1.98-1.1 2020-01-19 [1]
## Rdpack 0.11-1 2019-12-14 [2]
## readr * 1.4.0 2020-10-05 [1]
## readxl * 1.3.1 2019-03-13 [2]
## rematch 1.0.1 2016-04-21 [2]
## remotes 2.1.0 2019-06-24 [2]
## reprex 0.3.0 2019-05-16 [2]
## reshape2 1.4.3 2017-12-11 [2]
## reticulate 1.14 2019-12-17 [2]
## rio 0.5.16 2018-11-26 [1]
## rlang 0.4.8 2020-10-08 [1]
## rmarkdown 2.0 2019-12-12 [2]
## robustbase 0.93-6 2020-03-23 [1]
## ROCR 1.0-7 2015-03-26 [2]
## rprojroot 1.3-2 2018-01-03 [2]
## RSpectra 0.16-0 2019-12-01 [2]
## rstudioapi 0.11 2020-02-07 [1]
## rsvd 1.0.3 2020-02-17 [1]
## Rtsne 0.15 2018-11-10 [2]
## rvest 0.3.6 2020-07-25 [1]
## S4Vectors 0.24.2 2020-01-13 [2]
## sandwich 2.5-1 2019-04-06 [2]
## scales 1.1.0 2019-11-18 [2]
## scatterplot3d 0.3-41 2018-03-14 [1]
## sctransform 0.2.1 2019-12-17 [2]
## SDMTools 1.1-221.2 2019-11-30 [2]
## sessioninfo 1.1.1 2018-11-05 [2]
## Seurat * 3.1.2 2019-12-12 [2]
## SingleCellExperiment 1.8.0 2019-10-29 [2]
## smoother 1.1 2015-04-16 [1]
## sn 1.5-4 2019-05-14 [2]
## sp 1.4-2 2020-05-20 [1]
## stringi 1.5.3 2020-09-09 [1]
## stringr * 1.4.0 2019-02-10 [1]
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## survival 3.1-8 2019-12-03 [3]
## testthat 2.3.2 2020-03-02 [1]
## TFisher 0.2.0 2018-03-21 [2]
## TH.data 1.0-10 2019-01-21 [2]
## tibble * 3.0.4 2020-10-12 [1]
## tidyr * 1.1.2 2020-08-27 [1]
## tidyselect 1.1.0 2020-05-11 [1]
## tidyverse * 1.3.0 2019-11-21 [2]
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## TTR 0.23-6 2019-12-15 [1]
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## vcd 1.4-7 2020-04-02 [1]
## vctrs 0.3.5 2020-11-17 [1]
## VIM 6.0.0 2020-05-08 [1]
## viridis * 0.5.1 2018-03-29 [2]
## viridisLite * 0.3.0 2018-02-01 [2]
## withr 2.3.0 2020-09-22 [1]
## xfun 0.12 2020-01-13 [2]
## xml2 1.3.2 2020-04-23 [1]
## xts 0.12-0 2020-01-19 [1]
## XVector 0.26.0 2019-10-29 [2]
## yaml 2.2.1 2020-02-01 [1]
## zip 2.0.4 2019-09-01 [1]
## zlibbioc 1.32.0 2019-10-29 [2]
## zoo 1.8-7 2020-01-10 [2]
## source
## CRAN (R 3.6.2)
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##
## [1] /home/uhlitzf/R/lib
## [2] /usr/local/lib/R/site-library
## [3] /usr/local/lib/R/library